title: Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities.
abstract: The segmentation of gait signals into single steps is an important basis for objective gait analysis. Only a precise detection of step beginning and end enables the computation of step parameters like step height, variability and duration. A special challenge for the application is the accurateness of such an algorithm when based on signals from daily live activities. In this study, gyroscopes were attached laterally to sport shoes to collect gait data. For the automated step segmentation, subsequence Dynamic Time Warping was used. 35 healthy controls and ten patients with Parkinson's disease performed a four times ten meter walk. Furthermore 4 subjects were recorded during different daily life activities. The algorithm enabled counting steps, detecting precisely step beginning and end and rejecting other movements. Results showed a recognition rate of steps during ten meter walk exercises of 97.7% and in daily life activities of 86.7%. The segmentation procedure can be used for gait analysis from daily life activities and can constitute the basis for computation of precise step parameters. The algorithm is applicable for long-term gait monitoring as well as for analyzing gait abnormalities.
authors.forename: Jens
authors.initials: J
authors.lastname: Barth
authors.forename: Cacilia
authors.initials: C
authors.lastname: Oberndorfer
authors.forename: Patrick
authors.initials: P
authors.lastname: Kugler
authors.forename: Dominik
authors.initials: D
authors.lastname: Schuldhaus
authors.forename: Jurgen
authors.initials: J
authors.lastname: Winkler
authors.forename: Jochen
authors.initials: J
authors.lastname: Klucken
authors.forename: Bjorn
authors.initials: B
authors.lastname: Eskofier
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D005684:Gait; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods; D010300:Parkinson Disease / Q000503:physiopathology*; D012775:Shoes; D059015:Wireless Technology